Energy Demand Research Articles

Impact of urbanization, economic growth, FDI, and trade openness on energy demand in Ireland: An ARDL approach

Abstract This research study uses an autoregressive distributed lag (ARDL) paradigm to examine the effects of trade openness, urbanization, economic growth, and foreign direct investment (FDI) on energy demand in Ireland. This thorough theoretical framework anticipates a relationship between energy demand and trade, foreign direct investment, and urbanization. This study demonstrates both short- and long-run cointegration among the variables employing an ARDL (autoregressive distributed lag) bounds-testing method for the data set period from 1972 to 2021. Additionally, Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegrating Regression (CCR) were utilized in the study to verify the results' robustness. Nonetheless, this analysis discovers that trade openness and rising GDP (Gross Domestic Product) per capita increase energy consumption. The results show that for every 1% increase in GDP, energy consumption increases by 0.967%. On the flip side, energy consumption in Ireland is declining due to increased urbanization and foreign direct investment. The results show that for every 1% increase in urbanization, energy usage decreases by 1.738%. The investigation shows that Ireland's energy usage is strongly impacted by trade liberalization, FDI, urbanization, and economic expansion. Findings like these can help policymakers and stakeholders manage and optimize energy usage in the context of urbanization, economic expansion, foreign direct investment, and global trade. The report recommends sustainable energy planning and policy initiatives to strike a healthy balance between meeting the increasing demand for energy and protecting the environment.

Can Renewable Energy Be a Driving Factor for Economic Stability? An In-depth Study of Sector Expansion and Economic Dynamics

India has emerged as one of the world's most appealing locations for renewable energy development. It has set lofty renewable energy goals to reach 450 gigawatts (GW) capacity by 2030. These aims indicate India's determination to move to greener and more sustainable energy sources. India has been investing in R&D to promote technological innovation in renewable energy. This includes improvements to solar photovoltaic technology, wind energy, energy storage technologies, and smart grid systems. Innovation is critical for improving efficiency, lowering prices, and increasing the reliability of renewable energy sources. This paper aims to analyse the linkages between economic growth and renewable energy usage in India. For this, the Granger Causality technique is adopted, and it is found that no short-run causality exists among the economic growth and RE installed capacity. However, Industrial Production Granger Causes both GDP and Renewable Energy Capacity. When the stock price data of the last five years of top renewable energy companies was also collected, it was found that all the companies are showing an upward trend. While renewable energy is growing rapidly, especially solar and wind power, it is insufficient to meet the bulk of India's energy demands. Renewables contribute to reducing carbon emissions and diversifying the energy mix, but they still account for a smaller percentage compared to thermal power.

Transient stability enhancement of Beles to Bahir Dar transmission line using adaptive neuro-fuzzy-based unified power flow controller

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement.

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:p$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:q$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:r$$\\end{document}. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

ZnO-NaNO3 nanocomposites for solar thermal energy storage systems

High-temperature phase change materials (PCMs) with good energy storage density and thermal conductivity are needed to utilize solar thermal energy effectively to meet industrial thermal energy demands. Composite PCMs containing a material of higher thermal conductivity and an inorganic high-temperature PCM can be explored to meet these requirements. Accordingly, a high-temperature, composite inorganic PCM (ZnO-NaNO3) with enhanced thermophysical properties was prepared, and its energy storage potential was investigated experimentally. A maximum thermal conductivity enhancement of 22.7% was achieved at 200 °C for 2 wt% ZnO-NaNO3 nanocomposite. The increase in thermal conductivity at higher temperatures may be attributed to the formation of ordered sodium nitrate layers on the nanoparticle surfaces. The increase in surface area and surface energy due to the addition of ZnO nanoparticles increased the specific heat of the nanocomposite in both the solid and liquid phases (43.5% in the liquid phase for 2 wt% ZnO-NaNO3). Thus, the addition of ZnO nanoparticles to NaNO3 increased its energy storage capacity. The addition of ZnO nanoparticles to NaNO3 did not affect the onset, peak or endset temperature during melting and freezing. Moreover, 2 wt% ZnO-NaNO3 exhibited cyclic stability even after 500 cycles and thus has potential as an energy storage medium.

Investigating the feasibility of agro-waste briquettes as a sustainable energy source in Borno state Nigeria as pathways for post-conflict and instability recovery

The growing energy demand and lack of access to clean energy sources call for the development of more energy for cooking in households, briquettes produced using agricultural materials and related biomass have the capacity for utilization as an energy source. This study aims to introduce briquettes as an alternative energy source for cooking within conflict-affected communities in Borno State, northeastern Nigeria, where factors relating to the high cost of charcoal, security constraints, and the environmental impact of tree cutting have sparked apprehension and raised concerns. The study demonstrates the likelihood of adopting briquettes as a cooking fuel alternative in communities recovering from insurgency. Using assessment tools such as household surveys, Key Informant Interviews (KIIs), and Focus Group Discussions (FGDs), involving a total of 536 questionnaires administered to respondents; KII was administered to 37 respondents and 9 FGDs with cooperatives and dealers in traditional fuel were conducted. From the results 73% of the community relies on charcoal as their primary fuel source, since the majority (68.7%) appeared to be low-income households, 24.1% reported that the high cost of charcoal is a major hindrance to continued usage. However, despite the communities showing potential in briquettes, only 3.7% are aware of briquettes as an alternative option for cooking energy indicating the adoption of briquettes being low in the region. A situational assessment was carried and sensitization strategies were recommended as means to enhance livelihoods and build resilience within the community.

Evaluating Microgrid Investments: Introducing the MPIR Index for Economic and Environmental Synergy

In view of the increasing environmental challenges and the growing demand for sustainable energy solutions, the optimization of microgrid systems with regard to economic efficiency and environmental compatibility is becoming ever more important. This paper presents the Microgrid Performance and Investment Rating (MPIR) index, a novel assessment framework developed to link economic and environmental objectives within microgrid configurations. The MPIR index evaluates microgrid configurations based on five critical dimensions: financial viability, sustainability, regional renewable integration readiness, energy demand, and community engagement, facilitating comprehensive and balanced decision making. The current cases focus on the area of Greece; however, the model can have a wider application. Developed using a two-target optimization model, this index integrates various energy sources—including photovoltaics, micro-wind turbines, and different types of batteries—with advanced energy management strategies to assess and improve microgrid performance. This paper presents case studies in which the MPIR index is applied to different microgrid scenarios. It demonstrates its effectiveness in identifying optimal configurations that reduce the carbon footprint while maximizing economic returns. The MPIR index provides a quantifiable, scalable tool for stakeholders, not only advancing the field of microgrid optimization, but also aligning with global sustainability goals and promoting the transition to a more resilient and sustainable energy future.

LCA comparativo en distintos escenarios del edificio NZEB El Salvador, estimación de CO2 para el desempeño térmico

The recent IPCC 2023 report reiterates that humansare responsible for global warming over the past 200 years, causing a rise temperature of 1.1°C above pre-industrial levels, as well as an increase in energy demand, which can be avoided with mitigation options, reducing its use in all sectors, especially in the building sector. One of the options is the design based on the NZEB concept, as it has been the case of the laboratory built on the UCA campus, using passive and active energy efficiency strategies, studying the interaction between construction systems and energy savings. One of the NZEB El Salvador project research objectives, was to study the thermal performance of different construction scenarios, therefore, the present work presents the results of the Life-cycle assessment(LCA) in three proposals for popular construction systems in El Salvador, regulated by the national regulatory framework, comparing them with the baseline of its current operation, through 3 iterative calculation tools: structural, thermal and carbon footprint estimation, managing to find importantfindings on how vernacular systems meet the NZEB standard. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 160-166.

Analysis of Electricity Supply and Demand Balance in Residential Microgrids Integrated with Micro-CAES in Northern Portugal

As global energy demand continues to rise, integrating renewable energy sources (RES) into power systems has become increasingly important. However, the intermittent nature of RES, such as solar and wind, presents challenges for maintaining a stable energy supply. To address this issue, energy storage systems are essential. One promising technology is micro-compressed air energy storage (micro-CAES), which stores excess energy as compressed air and releases it when needed to balance supply and demand. This study investigates the integration of micro-CAES with RES in a 19-home microgrid in northern Portugal. The research aims to evaluate the effectiveness of a microgrid configuration that includes 100 kW of solar PV, 70 kW of wind power, and a 50 kWh micro-CAES system. Using real-world data on electricity consumption and local renewable potential, a simulation is conducted to assess the performance of this system. The findings reveal that this configuration can supply up to 68.8% of the annual energy demand, significantly reducing reliance on the external grid and enhancing the system’s resilience. These results highlight the potential of micro-CAES to improve the efficiency and sustainability of small-scale renewable energy systems, demonstrating its value as a key component in future energy solutions.

Numerical and Experimental Power Output Estimation for a Small-Scale Hinged Wave Energy Converter

Wave energy converters (WECs) integrated into breakwaters present a promising solution for combining coastal protection with renewable energy generation, addressing both energy demands and environmental concerns. Additionally, this integration offers cost-sharing opportunities, making the overall investment more economically viable. This study explores the potential of a hinged point-absorber WEC, specifically designed as a floating hinged half-sphere, by assessing the device’s power output and comparing two different breakwater configurations. To evaluate the device’s performance, a comprehensive numerical and experimental approach was adopted. Numerical simulations were carried out using a radiation-diffraction model, a time domain tool for analyzing wave–structure interactions. These simulations predicted average power outputs of 25 kW for sloped breakwaters and 18 kW for vertical breakwaters located at two strategic sites: the Port of Leixões and the mouth of the Douro River in Portugal. To validate these predictions, a 1:14 scale model of the WEC was constructed and subjected to testing in a wave–current flume, replicating different sea-state conditions. The experimental results closely aligned with the numerical simulations, demonstrating a good match in terms of relative error and relative amplitude operator (RAO). This alignment confirms the reliability of the predictive model. These findings support the potential of integrating WECs into breakwaters, contributing to port energy self-sufficiency and decarbonization.

Heating Energy Performance Gap in Vulnerable Households: Identification and Impact of Associated Variables

Reducing energy consumption in the construction sector is urgently needed. In Chile, where income distribution is unequal and the cost of energy is high, energy demand is seriously affected, especially in vulnerable households. Hence, it is essential to establish public policies with more realistic energy-saving goals to address this situation. However, reliably predicting the energy performance of buildings remains a challenge. For this reason, this study aims to identify and evaluate the impact of the variables associated with energy performance in vulnerable households in Central-Southern Chile and propose values that would reduce the gap. A sensitivity analysis was conducted to achieve this, adjusting the energy performance parameters in a base model with data analyzed using local standards. In addition, field information was collected in 93 households to obtain the actual energy consumption. The main results show that the variables that most impacted performance were infiltration, COP, heating setpoints, and schedules, which generated a 60% difference between the theoretical and actual consumption.

Magnetic Nanoparticles and Radio Frequency Induction: From Specific Heating to Magnetically Induced Catalysis

AbstractThis comprehensive review explores the potential of radio frequency (RF) induction heating in chemical reactions, explicitly focusing on magnetically induced catalysis using magnetic nanoparticles (NPs). We trace the recent historical progress of induction heating and highlight the advancements in liquid and gas‐phase reactions, particularly in its integration with heterogeneous catalysis. The review finds that induction heating profoundly impacts reaction kinetics, and selectivity, and can even reduce overpotentials in electrocatalytic processes. A final outlook unveils the challenges and opportunities associated with aspects such as fundamental research and reactor design, with a particular focus on expanding its use to higher pressures and necessary optimizations to improve energy efficiency. Moreover, it highlights the pressing need for standardized reporting in induction‐heated catalysis. The study underscores the significance of this brand‐new field in developing efficient and sustainable catalytic processes, which are essential for meeting the growing demand for clean energy and sustainable chemical production.

Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication

The depletion of conventional light petroleum reserves has intensified the search for alternative sources, notably, low-quality heavy oils and byproducts from heavy crude processing, to meet the global demand for fuels, energy, and petrochemicals. Heavy crude oil (HO) and extra heavy crude oil (EHO) represent nearly 70% of the world’s reserves but require extensive upgrading to satisfy refining and petrochemical specifications. Their high asphaltene content results in elevated viscosity and reduced API gravity, posing significant challenges in extraction, transportation, and refining. Advanced catalytic approaches are crucial for efficient asphaltene removal and the conversion of heavy feedstocks into valuable light fractions. Kaolin, an aluminosilicate mineral, has emerged as a key precursor for zeolite synthesis and a promising catalyst in upgrading processes. This article provides a comprehensive exploration of kaolin’s geological origins, chemical properties, and structural characteristics, as well as the various modification techniques designed to improve its catalytic performance. Special focus is given to its application in the transformation of heavy crudes, particularly in facilitating asphaltene breakdown and enhancing light distillate yields. Finally, future research avenues and potential developments in kaolin-based catalysis are discussed, emphasizing its vital role in addressing the technological challenges linked to the growing reliance on heavier crude resources.

Model development and heat transfer characteristics in renewable energy systems conveying hybrid nanofluids subject to nonlinear thermal radiation

PurposeIn today’s world, the demand for energy to power industrial and domestic activities is increasing. To meet this need and enhance thermal transport, solar energy conservation can be tapped into via solar collector coating for thermal productivity. Hybrid nanofluids (HNFs), which combine nanoparticles with conventional heat transfer fluids, offer promising opportunities for improving the efficiency and sustainability of renewable energy systems. Thus, this paper explores fluid modeling application techniques to analyze and optimize heat transfer enhancement using HNFs. A model comprising solar energy radiation with nanoparticles of copper (Cu) and alumina oxide (Al2O3) suspended in water (H2O) over an extending material device is developed.Design/methodology/approachThe model is formulated using conservation laws to build relevant equations, which are then solved using the Galerkin numerical technique simulated via Maple software. The computational results are displayed in various graphs and tables to showcase the heat transfer mechanism in the system.FindingsThe results reveal the thermal-radiation-boost heat transfer phenomenon in the system. The simulations of the theoretical fluid models can help researchers understand how HNFs facilitate heat transfer in renewable energy systems.Originality/valueThe originality of this study is in exploring the heat transfer properties within renewable energy systems using HNFs under the influence of nonlinear thermal radiation.

Experimental evaluation of subjective thermal perceptions for different local window screenings

In tropical climate, restricting convective gain while allowing cross ventilation creates paradoxical difficulty regarding indoor thermal comfort. Changing global climate gives new dimensions to the scenario results in subsequent pressure on increasing energy demand towards thermal comfort. Passive techniques can be a good way to dealt with the problem. However, occupants’ thermal perception may vary from person to person for a definite indoor environmental setup. Perception of subjective thermal comfort is crucial for human health and wellbeing which in turns effects performance. In the current study evaluation of subjective thermal performance regarding improved indoor thermal comfort has been studied for three different local window screening: i) plastic bottle with its wider face towards wind direction, ii) plastic bottle with its narrower face towards wind direction and iii) perforated bamboo screen. Six subjects: four female and two male (ages between 22-24), have been investigated for 30mins. in an experimental simulation chamber for three consecutive days. Questionnaire regarding thermal sensation and perception has been prepared to collect subjective response at 10mins. interval. From the experiment it is seen that subjective responses varies for different window screenings as well as for a specific indoor thermal condition created with definite screening. Subjects find the indoor environment comfortable for screening with plastic bottle with its wider face towards wind direction compared to other two. Bamboo screening has comparatively better performance than the screening plastic bottle with its narrower face towards wind direction. The current analysis only considers the thermal sensation of the subject and further extension of study considering factors like the subject's site-specific thermal sensation and other psychological effects can contribute towards improving indoor thermal comfort in tropics towards human health and wellbeing.

Modelling and optimization of combined supercritical carbon dioxide Brayton cycle and organic Rankine cycle for electricity and hydrogen production

Hydrogen as a clean fuel, is key energy carrier for sustainable development, and its production via water electrolysis is most desirable for its carbon free nature. Nuclear power provides good opportunity to satisfy the huge energy demand associated with the process, in addition to electrical power generation. Modelling an efficient and cost effective nuclear based power and Hydrogen cogeneration system and optimizing it to enhance its performance is the core academic problem addressed in the present study. It proposes a power plant cycle aided by a nuclear reactor, providing 300 MW thermal power input. Supercritical CO2 Brayton cycle is used as the main cycle, instead of traditional Helium Brayton cycle or steam Rankine cycle. To increase the cycle efficiency, by reducing the compression work and by reducing the cold source loss, main compression intercooling and waste heat recovery by an Organic Rankine Cycle (ORC) is introduced together with recompression Brayton cycle arrangement. The power coming from recovered waste heat goes to a Proton Exchange Membrane Electrolyzer (PEME), which facilitates the production of Hydrogen by splitting water via electrolysis. Additional attention is given to ORC fluid selection by employing and comparing the performance of widely acknowledged R245fa together with its more eco friendly replacements, R1233Zd(E) and R1234Ze(Z). The evaluation of the system performance is conducted through the lenses of Energy, Exergy, and Economics. Essential parameters are scrutinized to gauge their impact on two pivotal objectives. Overall Exergy Efficiency(ηex) takes care of the thermodynamic performance and the economic aspect is scrutinized by Levelized Cost of Energy (LCOEn). Notably, by employing R245fa as the ORC working fluid, the system attains peak Energy efficiency of 42.13% and Exergy efficiency of 58.32%, and a minimum LCOEn of 0.063 USD/kWh. Of all the components associated with the system nuclear reactor tops the chart for maximum exergy destroyed(40.8% of total) as well as maximum investment cost(65.7% of total). Pump contributes least to exergy destruction(0.03% of the total) and heat exchangers hold least share(1.1% of total) for investment cost. Furthermore, a multiobjective optimization exercise is executed to fine-tune the system’s operational parameters. This optimization results in a 3.12% increase in Overall Exergy Efficiency and a 9.79% decrease in LCOEn compared to the base operating condition with R245fa used as the ORC fluid. The ηex & LCOEn in best possible operating condition are 58.25% & 0.0645 USD/kWh using R245fa as ORC fluid, 58.23% & 0.0645 USD/kWh using R1233Zd(E) as ORC fluid and 58.22% & 0.0644 USD/kWh using R1234Ze(Z) as ORC fluid. If same amount of electrical Power is supplied to PEME for both with and without ORC configuration, having an ORC is advantageous both in terms of superior Exergy Efficiency and lower LCOEn. The proposed configuration is also economically more lucrative with higher Net Present Value(NPV) and Benefit to Cost Ratio(BCR). For example, when PEME power input for both the cases is 10 MW, proposed system has NPV = 201.79 Million USD & BCR = 1.70 compared to NPV = 164.03 Million USD & BCR = 1.58 for without ORC configuration. Even if in without ORC configuration, no electrical power is supplied to the electrolyzer, adding an ORC to produce Hydrogen will still achieve maximum 1.57% Exergy Efficiency increment, compared to only electrical power production by sCO2 Brayton cycle. It is seen that in base condition, the NPV is still superior for the proposed configuration (203.03 to 189.32 Million USD), and the BCR dips only slightly from 1.72 to 1.71.

Enhancing Electrochemical Performance of Si@CNT Anode by Integrating SrTiO3 Material for High-Capacity Lithium-Ion Batteries

Silicon (Si) has attracted worldwide attention for its ultrahigh theoretical storage capacity (4200 mA h g−1), low mass density (2.33 g cm−3), low operating potential (0.4 V vs. Li/Li+), abundant reserves, environmentally benign nature, and low cost. It is a promising high-energy-density anode material for next-generation lithium-ion batteries (LIBs), offering a replacement for graphite anodes owing to the escalating energy demands in booming automobile and energy storage applications. Unfortunately, the commercialization of silicon anodes is stringently hindered by large volume expansion during lithiation–delithiation, the unstable and detrimental growth of electrode/electrolyte interface layers, sluggish Li-ion diffusion, poor rate performance, and inherently low ion/electron conductivity. These present major safety challenges lead to quick capacity degradation in LIBs. Herein, we present the synergistic effects of nanostructured silicon and SrTiO3 (STO) for use as anodes in Li-ion batteries. Si and STO nanoparticles were incorporated into a multiwalled carbon nanotube (CNT) matrix using a planetary ball-milling process. The mechanical stress resulting from the expansion of Si was transferred via the CNT matrix to the STO. We discovered that the introduction of STO can improve the electrochemical performance of Si/CNT nanocomposite anodes. Experimental measurements and electrochemical impedance spectroscopy provide evidence for the enhanced mobility of Li-ions facilitated by STO. Hence, incorporating STO into the Si@CNT anode yields promising results, exhibiting a high initial Coulombic efficiency of approximately 85%, a reversible specific capacity of ~800 mA h g−1 after 100 cycles at 100 mA g−1, and a high-rate capability of 1400 mA g−1 with a capacity of 800 mA h g−1. Interestingly, it exhibits a capacity of 350 mAh g−1 after 1000 lithiation and delithiation cycles at a high rate of 600 mA hg−1. This result unveils and sheds light on the design of a scalable method for manufacturing Si anodes for next-generation LIBs.

Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars.

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans' robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%-65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.IMPORTANCEThe robustness of B. coagulans makes it a valuable microorganism for biotechnology applications; yet, this phenotype is not well understood at the cellular level. Through phenotypic characterization and systems analysis, this study elucidates the functional genomics and robustness of a novel, undomesticated strain, B. coagulans B-768, capable of utilizing inhibitory switchgrass biomass hydrolysates. The genome of B-768, enriched with carbohydrate metabolism genes, demonstrates high regulatory capacity. The coordination of proteome reallocation in Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) is critical for effective cell growth, sugar utilization, and lactate production via overflow metabolism. Overall, B-768 is a novel, robust, and promising B. coagulans strain that can be harnessed as a microbial biomanufacturing platform to produce chemicals and fuels from biomass hydrolysates.